Pandas Series vs DataFrame

Series: One-dimensional labeled array.

• Shape: (n,) → single axis of length n.
• Can store numbers, strings, or other data types.
• Example:

import pandas as pd
s = pd.Series([22, 24, 19], index=['Day1', 'Day2', 'Day3'])

• DataFrame: Two-dimensional labeled data structure (rows × columns).

  • Shape: (n, m) → n rows, m columns.
  • Each column is a Series.
  • Example:

  df = pd.DataFrame({'Temperature': [22, 24, 19]}, index=['Day1', 'Day2', 'Day3'])

• Single-column DataFrame vs Series:

  • df with one column is still 2D → shape (n,1).
  • df['Temperature'] or df.iloc[:,0] converts it to Series (1D → shape (n,)).
  • Some functions require 1D Series; others require 2D DataFrame.

Rule of thumb:

• Use Series for single-variable operations.
• Use DataFrame for multiple columns or when 2D operations are needed.